SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2591–2600 of 3874 papers

TitleStatusHype
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation—0
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution—0
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding—0
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
A Survey on Deep learning based Document Image Enhancement—0
Feature-based Recognition Framework for Super-resolution Images—0
Towards Super-Resolution CEST MRI for Visualization of Small StructuresCode0
Uncertainty-Driven Loss for Single Image Super-Resolution—0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified